Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 44
... functions . Central to the decision - theoretic treatment is the specifi- cation of a loss function , λ ( ij ) . Here λ ( ij ) is a function defined for i = 1 , R and j = 1 , . . = 1 , ... , R and represents the loss incurred when the ...
... functions . Central to the decision - theoretic treatment is the specifi- cation of a loss function , λ ( ij ) . Here λ ( ij ) is a function defined for i = 1 , R and j = 1 , . . = 1 , ... , R and represents the loss incurred when the ...
Sivu 45
... loss of λ ( ij ) , where j is the actual category of pattern X. The ... function , we can obtain a set of equiva- lent , but simpler , discriminant ... function of j , p ( X | j ) is often called the likelihood of j with respect to X ; p ...
... loss of λ ( ij ) , where j is the actual category of pattern X. The ... function , we can obtain a set of equiva- lent , but simpler , discriminant ... function of j , p ( X | j ) is often called the likelihood of j with respect to X ; p ...
Sivu 46
... loss function We have shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple ... functions can be 46 PARAMETRIC TRAINING METHODS A special loss function,
... loss function We have shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple ... functions can be 46 PARAMETRIC TRAINING METHODS A special loss function,
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |